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Questions and Answers
What is the primary function of the source encoder in an image compression model?
What type of compression is used in applications that require no error in compression?
What is the primary advantage of Huffman coding?
What is the typical compression ratio (CR) expected from lossless compression techniques?
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What type of redundancy is exploited by the source encoder in an image compression model?
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What is the goal of image compression?
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What is the name of the technique that yields the smallest number of code symbols per source symbol?
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What type of compression is used in applications that require a high compression ratio?
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What is the average code length without compression?
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What is the main purpose of image compression?
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What is the purpose of Huffman coding?
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What type of compression is suitable for medical image applications?
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What is one of the applications of image compression?
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What is the main advantage of transform coding techniques?
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What type of compression is used to ensure that the decompressed image is identical to the original image?
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What is the main difference between lossless and lossy compression?
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What is the primary concern in image compression?
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What is the benefit of using image compression?
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What is the main disadvantage of lossy compression?
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What is the purpose of the Huffman code?
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What is an example of an error-free compression application?
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What is the average code length with Huffman coding?
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What is the purpose of Huffman coding?
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What is the effect of image compression on data transmission?
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Study Notes
Image Compression Model
- The source encoder removes redundancy (coding, inter-pixel, psycho-visual), while the channel encoder ensures robustness against channel noise.
Compression Types
- There are two types of compression: error-free (lossless) compression and lossy compression.
Error-Free (Lossless) Compression
- Required in applications where no error is allowed (medical, business documents, etc.).
- Compression ratios (CR) of 2 to 10 can be achieved.
- Techniques used: Huffman codes, Arithmetic coding, 1D and 2D run-length encoding, Loss-less Predictive Coding, and Bit-Plane Coding.
Huffman Coding
- A popular technique for removing coding redundancy.
- Yields the smallest number of code symbols per source symbol.
- The resulting code is optimal.
Example of Huffman Codes
- Average code length (Lavg) is calculated using probability and code lengths.
Image Compression Importance
- Everyday, a vast amount of information is stored, processed, and transmitted.
- Image compression reduces data requirements to represent a digital image.
- It plays a crucial role in Video Conferencing, remote sensing, satellite TV, FAX, documentation, image database, and medical imaging.
Lossy Compression
- Lossy compression gives a more compact representation of an image at the cost of some data loss.
- Although the original image cannot be fully reconstructed, the degradation may not affect the purpose of using the image.
- Transform coding techniques are useful lossy techniques: Discrete Fourier, Wavelet, Discrete Cosine Transforms.
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Description
Image compression is a crucial aspect of data storage and transmission. This quiz covers the basics of image compression, its importance, and its applications.
